Modern optical microscopy has grown into a multi-dimensional imaging tool. It is now possible to record dynamic processes in living specimens in their spatial context and temporal order, yielding information-rich 5-D images (3-D space, time, spectra).Of particular interest are complex and dynamic tissuemicroenvironments that play critical roles in health and disease, e.g., tumors, stem-cell niches, brain tissue surrounding neuroprosthetic devices, retinal tissue, cancer stem-cell niches, glands, and immune system tissues. The task of analyzing these images exceeds human ability due to the sheer volume of the data (images routinely exceed 20GB in size), its structural complexity, and the dynamic behaviors of cells and organelles. First, there is a need for automated systems to assist the human analyst to map the tissue anatomy, quantify structural associations, identify critical events, map event locations and timing to the tissue anatomic context, identify and quantify spatial and temporal dependencies, produce meaningful summaries of multivariate measurement data, and compare perturbed and normal datasets for testing hypotheses, exploration, and systems modeling. Beyond automation, there is a need for ³computational sensing² of tissue patterns and cell behaviors that are too subtle for the human visual system to detect. In this talk, I will describe large-scale application of image processing, active machine learning, multivariate clustering, and parallel computation methods that enable scalable analysis of multi-dimensional microscopy data. A particularly valuable application of these methods is to validate the large-scale automated analysis results. All of the software from this work is free and open source (www.farsight-toolkit,org).